Accelerating Transcriptome Analysis with GPUs and Machine Learning

EasyChair Preprint 13880

Abstract

The advent of high-throughput sequencing technologies has revolutionized transcriptome analysis, enabling researchers to delve into the complexities of gene expression with unprecedented detail. However, the massive volumes of data generated present significant computational challenges, necessitating the development of more efficient analysis techniques. This paper explores the integration of Graphics Processing Units (GPUs) and machine learning to accelerate transcriptome analysis, highlighting the potential for enhanced performance and deeper insights. By leveraging the parallel processing capabilities of GPUs, we demonstrate significant reductions in computational time for tasks such as read alignment, differential expression analysis, and gene regulatory network inference. Additionally, machine learning algorithms are employed to improve the accuracy and predictive power of transcriptomic models, facilitating the identification of novel biomarkers and therapeutic targets. Through a series of benchmark studies, we compare traditional CPU-based approaches with GPU-accelerated methods, showcasing the transformative impact on speed and scalability. Our findings suggest that the combination of GPUs and machine learning not only optimizes the computational efficiency of transcriptome analysis but also opens new avenues for personalized medicine and advanced genomic research.

Keyphrases: Accelerated sequence analysis, Bioinformatic algorithms, Computational Proteomics, Computational genomics, Deep learning in bioinformatics, GPU-accelerated machine learning, GPU-based bioinformatics, Genomic data processing, High Performance Computing, Machine learning in computational biology

@booklet{EasyChair:13880,
  author    = {Abey Litty},
  title     = {Accelerating Transcriptome Analysis with GPUs and Machine Learning},
  howpublished = {EasyChair Preprint 13880},
  year      = {EasyChair, 2024}}